Discovering gene–environment interactions in Glioblastoma through a comprehensive data integration bioinformatics method

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Abstract

Glioblastoma multiforme (GBM) is the most common and aggressive type of human brain tumor. Although considerable efforts to delineate the underlying pathophysiological pathways have been made during the last decades, only very limited progress on treatment have been achieved because molecular pathways that drive the aggressive nature of GBM are largely unknown. Recent studies have emphasized the importance of environmental factors and the role of gene–environment interactions (GEI) in the development of GBM. Factors such as small sample sizes and study costs have limited the conduct of GEI studies in brain tumors however. Additionally, advances in high-throughput microarrays have produced a wealth of information concerning molecular biology of glioma. In particular, microarrays have been used to obtain genetic and epigenetic changes between normal non-tumor tissue and glioma tissue. Due to the relative rarity of gliomas, microarray data for these tumors is often the product of small studies, and thus pooling this data becomes desirable. To address the challenge of small sample sizes and GEI study difficulties, we introduce a comprehensive bioinformatics method using genetic variations (copy number variations and small-scale variations) and environmental data integration that links with Glioblastoma (GEG) to identify: (1) genes that interact with chemicals and have genetic variants linked to the development of GBM, (2) important pathways that may be influenced by environmental exposures (or endogenous chemicals), and (3) genes with variants in GBM that have been understudied in relation to GBM development. The first step in our GEG method identified genes responsive to environmental exposures using the Environmental Genome Project, Comparative Toxicology, and Seattle SNPs databases. These environmentally responsive genes were then compared to a curated list of genes containing copy number variation and/or mutations in GBM. This comparison produced a list of genes responsive to the environment and important to GBM that was then further analyzed using gene networking tools such as RSpider, Cytoscape, and DAVID. Using this GEG bioinformatics method we were able to identify 173 genes with the potential to be involved in GEI that may be important to the development of GBM. Sixty five of these environmentally responsive genes have not been reported as important to GBM development, despite several of them having substantial potential for response to chemicals and subsequent disease related actions. The main biological functions of these 173 genes include signaling by Nerve Growth Factor, DNA Repair, Integrin Cell Surface Interactions, Biological Oxidations, Apoptosis, Synaptic Transmission, Cell Cycle Checkpoints, and Arachidonic Acid Metabolism. Importantly, some of these functions have been implicated in the development of several cancers, including glioma. In summary, our GEG bioinformatics approach revealed potential gene–environment interactions, and generated new data for hypothesis generation, in GBM.

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